arXiv (All)
open access
Neptune: An AI model for Global Ocean Subseasonal Prediction
Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, energy planning, and insurance. Achieving reliable predictions at these timescales requires representing the ocean and it
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- AI in Climate Change Mitigation#72860
- Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting#316255
- Learning Regional Snow Water Equivalent and Snow Height Variations from Sentinel-1 InSAR Acquisitions#1128876
- Advancing Hydrological Forecasting and Reservoir Management Through Model Calibration and SWOT Satellite Observation#983506
- Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.#25625
Record · ID 985945
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive